Quantum-Powered Optimization for Electric Vehicle Charging Infrastructure Deployment

TL;DR

This paper introduces a quantum annealing-based optimization model for efficiently determining the optimal placement of electric vehicle charging stations, demonstrating rapid solution times and robustness through real-world case studies.

Contribution

It presents a novel quantum computation approach for EVCS deployment optimization, leveraging quantum annealing to improve solution speed and quality.

Findings

Quantum annealing finds optimal EVCS placement within seconds.

Solution quality is robust to area shape and size.

Validated with real-world case study.

Abstract

The infrastructure development of electric vehicle charging stations (EVCS) is critical to the integration of electrical vehicles (EVs) into transportation systems, which requires significant investment and has long-term impact on the adoption of EVs. In this paper, a mathematical model is developed to identify the optimal placement of EVCS by utilizing a novel quantum annealing (QA) algorithm and quantum computation (QC). The objective of the optimization model is to determine the locations of EVCS that maximize their service quality for EV users. The model is validated using a real-world case study and solved using commercially available quantum computers from D-Wave. The case study shows that the QA algorithm can find the optimal placement of EVCS within seconds. The quality of the solutions obtained using QC is not sensitive to the shape or size of the area where EVCS are to be deployed.